Air quality modelling using chemometric techniques

The datasets of air quality parameters for three years (2012-2014) were applied. HACA gave the result of three different groups of similarity based on the characteristics of air quality parameters. DA shows all seven parameters (CO, O3, PM10, SO2, NOx, NO and NO2) gave the most significant variables after stepwise backward mode. PCA identifies the major source of air pollution is due to combustion of fossil fuels in motor vehicles and industrial activities. The ANN model shows a better prediction compared to the MLR model with R2 values equal to 0.819 and 0.773 respectively. This study presents that the chemometric techniques and modelling become an excellent tool in API assessment, air pollution source identification, apportionment and can be setbacks in designing an API monitoring network for effective air pollution resources management

Air quality modelling using chemometric techniques

The datasets of air quality parameters for three years (2012-2014) were applied. HACA gave the result of three different groups of similarity based on the characteristics of air quality parameters. DA shows all seven parameters (CO, O3, PM10, SO2, NOx, NO and NO2) gave the most significant variables after stepwise backward mode. PCA identifies the major source of air pollution is due to combustion of fossil fuels in motor vehicles and industrial activities. The ANN model shows a better prediction compared to the MLR model with R2 values equal to 0.819 and 0.773 respectively.This study presents that the chemometric techniques and modelling become an excellent tool in API assessment, air pollution source identification, apportionment and can be setbacks in designing an API monitoring network for effective air pollution resources management.Keywords: air pollutant index; chemometric; ANN; ML

Air Quality Prediction - A Study Using Neural Network Based Approach

India is the 7th largest country by area and 2nd most populated country in the world. The reports prepared by IQAir revels that India is 3rd most polluted country after Bangladesh and Pakistan, on the basis of fine particulates (PM2.5) concentration for the year 2020. In this article, the quality of air in six Indian cities is predicted using data-driven Artificial Neural Network. The data was taken from the 'Kaggle' online source. For six Indian cities, 6139 data sets for ten contaminants (PM2.5, PM10, NO, NO2, NH3, CO, SO2, O3, C6H6 and C7H8) were chosen. The datasets were collected throughout the last five years, from 2016 to 2020, and were used to develop the predictive model. Two machine learning model are proposing in this study namely Artificial Intelligence (AI) and Gaussian Process Regression (GPR) The R-value of ANN and GPR models are 0.9611 and 0.9843 sequentially. The other performance indices such as RMSE, MAPE, MAE of the GPR model are 21.4079, 7.8945% and 13.5884, respectively. The developed model is quite useful to update citizens about the predicted air quality of the urban spaces and protect them from getting affected by the poor ambient air quality. It can also be used to find the proper abatement strategies as well as operational measures

Profiling and forecasting air pollutant index for Malaysia

Detection of poor air quality is important to provide an early warning system for air quality control and management. Thus, air pollutant index (API) is designed as a referential parameter in describing air pollution levels to provide information to enhance public awareness. This study aims to study API trend, time series forecasting methods, their performance evaluations and missing values effect for accurate early warning system using several approaches. First, a calendar grid visualization is introduced to effectively display API daily profiling for the whole of Malaysia in identifying the exact point of poor air quality. Second, comparisons between classical and modern forecasting methods, artificial neural network (ANN), fuzzy time series (FTS) and hybrid are carried out to identify the best model in Johor sampling stations; industrial, urban and suburban. Third, due to the issue of different perfect score in existing index measurement to evaluate forecast performance, a combination index measures is proposed alongside error magnitude measurement. Fourth, decomposition and spatial techniques are compared to find the effect of high accuracy imputations in API missing values. The finding presented that the air quality trend across the day, week, month and year are more significant due to the daily arrangement in the calendar grid visualization. The ANN model gives the best forecasting model of API for industrial and urban area while the hybrid model provide the best forecasting for suburban area. The forecasting performance for industrial and urban areas improve between 14% to 20% and 20% to 55% in error magnitude and index measurements, respectively when high accuracy missing values imputation is conducted. In conclusion, the profiling using calendar grid visualization is useful to guide the control actions of early warning system. Forecasting using modern methods give promising result in API and the improvements in measurements will assist in choosing the best forecasting method. Missing values imputation in data series can enhance the forecasting performance